Augmented Reality (AR) systems in which digital content is delivered to a user via a mobile computing device are well known. Known AR systems have, at best, basic means, such as absolute location or identification of a predetermined object, to determine whether a user should be notified that a particular real-world location/scene is suitable for generating digital content that the user will find personally relevant. Accordingly, conventional systems do not fully consider wider contextual and environmental parameters when deciding whether to provide the user with an automated notification of the availability of digital content that the user is highly likely to find relevant, should the user decide to subsequently invoke sensing of the real-world scene with the full set of available sensors on his mobile device.
Also, in conventional AR systems, an individual real-world scene has a limited set of digital content associated with it and that content is in a predetermined presentation style (graphics, video, audio, etc.). Any tailoring of the information presented by the content to user-specific needs, including but not limited to user preferences, is conducted based on a small number of general user preferences, explicitly indicated by the user. The digital content associated with each general user preference is stored in logically-distinct libraries and databases. Contextual and environmental information is not considered in the decision as to which information, and hence content, to provide to a particular user in a particular context or environment. Every user gets similar information and digital content, regardless of their detailed and implicit personal needs, parameters of their physical environment (e.g. ambient lighting level, background noise level, and so on) and their social context (e.g. alone, with friends, in a business meeting, and so on). Accordingly known AR systems do not automatically generate digital content where the information within the content and the content presentation style (e.g. how vision, sound and touch outputs are used) is tailored to detailed personal needs, including personal preferences, and important aspects of personal context/environment.
It would be desirable therefore to dynamically generate highly contextual and personalized digital content, especially but not exclusively for use in AR systems. Education and healthcare are examples of application areas where such highly contextual and personalized information and hence digital content would be particularly useful and relevant.